CAFDIM: A Group Convolution and Self-Attention Fusion-Based Dual-Domain Iterative Method for Sparse-View CT Reconstruction

OBJECTIVE: Sparse-view computed tomography (CT) reduces radiation dose and acquisition time by decreasing the number of projection views, but it also makes image reconstruction severely ill-posed, leading to structural distortion and severe artifacts. This study aims to develop an effective reconstruction framework for improving both projection-data fidelity and reconstructed image quality in sparse-view CT. APPROACH: We propose a group Convolution- and self-Attention Fusion-based Dual-domain Iterative Method (CAFDIM) for sparse-view CT reconstruction. CAFDIM follows a model-informed dual-domain iterative design. The framework consists of the Initialization Enhancement Network (IE-Net), Gradient Update Block (GUB), Projection-domain Repair Network (PR-Net), Image-domain Repair Network(IR-Net), and Momentum Update Block (MUB). The projection-domain branch employs a Deep Sparse Block (DSB) to enhance sparse projection features before full-view projection restoration, while the image-domain branch uses edge-guided residual refinement to improve anatomical structure preservation. To enhance local-global feature representation, a Convolution-Attention Fusion Block (CAFB) is embedded into both repair branches by combining group convolution with Pixel Shift Self-Attention (PSSA). RESULTS: Experiments on simulated and real clinical projection datasets demonstrate that CAFDIM effectively suppresses sparse-view artifacts, preserves anatomical structures, and achieves superior reconstruction accuracy, visual quality, and generalization ability compared with state-of-the-art methods. SIGNIFICANCE: CAFDIM provides an effective and efficient dual-domain reconstruction framework for sparse-view CT, showing strong potential for clinical applications in sparse-view and low-dose CT imaging.